Overview

Dataset statistics

Number of variables16
Number of observations13184
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 MiB
Average record size in memory143.0 B

Variable types

Numeric15
Categorical1

Alerts

song_name has a high cardinality: 9893 distinct valuesHigh cardinality
acousticness is highly overall correlated with energy and 1 other fieldsHigh correlation
energy is highly overall correlated with acousticness and 1 other fieldsHigh correlation
loudness is highly overall correlated with acousticness and 2 other fieldsHigh correlation
instrumentalness is highly overall correlated with loudnessHigh correlation
tempo is highly overall correlated with time_signatureHigh correlation
time_signature is highly overall correlated with tempoHigh correlation
song_name is uniformly distributedUniform
song_id has unique valuesUnique
song_popularity has 173 (1.3%) zerosZeros
instrumentalness has 5043 (38.3%) zerosZeros
key has 1520 (11.5%) zerosZeros
audio_mode has 4902 (37.2%) zerosZeros

Reproduction

Analysis started2022-11-24 22:18:22.138392
Analysis finished2022-11-24 22:19:10.168774
Duration48.03 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

song_id
Real number (ℝ)

Distinct13184
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9397.5361
Minimum0
Maximum18832
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2022-11-24T23:19:10.311376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile938.15
Q14690.75
median9406
Q314090.5
95-th percentile17940.85
Maximum18832
Range18832
Interquartile range (IQR)9399.75

Descriptive statistics

Standard deviation5443.0264
Coefficient of variation (CV)0.57919718
Kurtosis-1.1957666
Mean9397.5361
Median Absolute Deviation (MAD)4701.5
Skewness0.0090966736
Sum1.2389712 × 108
Variance29626537
MonotonicityNot monotonic
2022-11-24T23:19:10.512297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2231 1
 
< 0.1%
16583 1
 
< 0.1%
16908 1
 
< 0.1%
10155 1
 
< 0.1%
18150 1
 
< 0.1%
16694 1
 
< 0.1%
331 1
 
< 0.1%
18702 1
 
< 0.1%
16364 1
 
< 0.1%
17995 1
 
< 0.1%
Other values (13174) 13174
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
14 1
< 0.1%
ValueCountFrequency (%)
18832 1
< 0.1%
18830 1
< 0.1%
18829 1
< 0.1%
18828 1
< 0.1%
18827 1
< 0.1%
18825 1
< 0.1%
18824 1
< 0.1%
18822 1
< 0.1%
18821 1
< 0.1%
18820 1
< 0.1%

song_name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct9893
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Memory size103.1 KiB
Better
 
16
Promises (with Sam Smith)
 
13
FEFE (feat. Nicki Minaj & Murda Beatz)
 
12
Without Me
 
12
REEL IT IN
 
11
Other values (9888)
13120 

Length

Max length141
Median length83
Mean length16.634254
Min length1

Characters and Unicode

Total characters219306
Distinct characters255
Distinct categories16 ?
Distinct scripts8 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8030 ?
Unique (%)60.9%

Sample

1st rowManeater - Radio Edit
2nd rowBetter Off Alone
3rd rowSong That I Heard
4th rowZumba
5th rowBumper To Bumper

Common Values

ValueCountFrequency (%)
Better 16
 
0.1%
Promises (with Sam Smith) 13
 
0.1%
FEFE (feat. Nicki Minaj & Murda Beatz) 12
 
0.1%
Without Me 12
 
0.1%
REEL IT IN 11
 
0.1%
I Like It 11
 
0.1%
Taki Taki (with Selena Gomez, Ozuna & Cardi B) 11
 
0.1%
MIA (feat. Drake) 11
 
0.1%
ZEZE (feat. Travis Scott & Offset) 11
 
0.1%
Sunflower - Spider-Man: Into the Spider-Verse 10
 
0.1%
Other values (9883) 13066
99.1%

Length

2022-11-24T23:19:10.758088image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1716
 
4.1%
the 1132
 
2.7%
feat 1051
 
2.5%
you 741
 
1.8%
me 634
 
1.5%
i 517
 
1.2%
love 477
 
1.1%
my 377
 
0.9%
a 373
 
0.9%
in 356
 
0.8%
Other values (8104) 34759
82.5%

Most occurring characters

ValueCountFrequency (%)
28949
 
13.2%
e 20370
 
9.3%
a 13261
 
6.0%
o 13144
 
6.0%
i 11174
 
5.1%
t 10150
 
4.6%
n 10052
 
4.6%
r 8990
 
4.1%
l 6754
 
3.1%
s 6708
 
3.1%
Other values (245) 89754
40.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 139886
63.8%
Uppercase Letter 39954
 
18.2%
Space Separator 28949
 
13.2%
Other Punctuation 3703
 
1.7%
Close Punctuation 1709
 
0.8%
Open Punctuation 1709
 
0.8%
Decimal Number 1679
 
0.8%
Dash Punctuation 1458
 
0.7%
Other Letter 152
 
0.1%
Currency Symbol 41
 
< 0.1%
Other values (6) 66
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ا 9
 
5.9%
ج 3
 
2.0%
ر 3
 
2.0%
د 3
 
2.0%
ي 3
 
2.0%
ل 3
 
2.0%
2
 
1.3%
2
 
1.3%
2
 
1.3%
2
 
1.3%
Other values (113) 120
78.9%
Lowercase Letter
ValueCountFrequency (%)
e 20370
14.6%
a 13261
 
9.5%
o 13144
 
9.4%
i 11174
 
8.0%
t 10150
 
7.3%
n 10052
 
7.2%
r 8990
 
6.4%
l 6754
 
4.8%
s 6708
 
4.8%
u 5005
 
3.6%
Other values (41) 34278
24.5%
Uppercase Letter
ValueCountFrequency (%)
S 3206
 
8.0%
T 3040
 
7.6%
M 2994
 
7.5%
L 2574
 
6.4%
B 2488
 
6.2%
A 2186
 
5.5%
R 2062
 
5.2%
I 2038
 
5.1%
D 1959
 
4.9%
C 1900
 
4.8%
Other values (22) 15507
38.8%
Other Punctuation
ValueCountFrequency (%)
. 1519
41.0%
' 1065
28.8%
& 360
 
9.7%
, 342
 
9.2%
! 90
 
2.4%
/ 89
 
2.4%
? 79
 
2.1%
" 64
 
1.7%
* 34
 
0.9%
: 34
 
0.9%
Other values (7) 27
 
0.7%
Decimal Number
ValueCountFrequency (%)
0 365
21.7%
1 342
20.4%
2 333
19.8%
9 181
10.8%
5 95
 
5.7%
4 84
 
5.0%
3 78
 
4.6%
6 68
 
4.1%
8 67
 
4.0%
7 66
 
3.9%
Close Punctuation
ValueCountFrequency (%)
) 1672
97.8%
] 35
 
2.0%
2
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 1672
97.8%
[ 35
 
2.0%
2
 
0.1%
Currency Symbol
ValueCountFrequency (%)
$ 39
95.1%
¥ 1
 
2.4%
£ 1
 
2.4%
Math Symbol
ValueCountFrequency (%)
+ 7
53.8%
| 5
38.5%
< 1
 
7.7%
Final Punctuation
ValueCountFrequency (%)
35
89.7%
4
 
10.3%
Initial Punctuation
ValueCountFrequency (%)
4
80.0%
1
 
20.0%
Other Symbol
ValueCountFrequency (%)
® 3
75.0%
° 1
 
25.0%
Space Separator
ValueCountFrequency (%)
28949
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1458
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 3
100.0%
Modifier Letter
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 179834
82.0%
Common 39314
 
17.9%
Han 75
 
< 0.1%
Arabic 37
 
< 0.1%
Katakana 20
 
< 0.1%
Hangul 20
 
< 0.1%
Cyrillic 5
 
< 0.1%
Greek 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 20370
 
11.3%
a 13261
 
7.4%
o 13144
 
7.3%
i 11174
 
6.2%
t 10150
 
5.6%
n 10052
 
5.6%
r 8990
 
5.0%
l 6754
 
3.8%
s 6708
 
3.7%
u 5005
 
2.8%
Other values (67) 74226
41.3%
Han
ValueCountFrequency (%)
2
 
2.7%
2
 
2.7%
2
 
2.7%
2
 
2.7%
2
 
2.7%
2
 
2.7%
1
 
1.3%
1
 
1.3%
1
 
1.3%
1
 
1.3%
Other values (59) 59
78.7%
Common
ValueCountFrequency (%)
28949
73.6%
) 1672
 
4.3%
( 1672
 
4.3%
. 1519
 
3.9%
- 1458
 
3.7%
' 1065
 
2.7%
0 365
 
0.9%
& 360
 
0.9%
, 342
 
0.9%
1 342
 
0.9%
Other values (39) 1570
 
4.0%
Hangul
ValueCountFrequency (%)
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Other values (10) 10
50.0%
Katakana
ValueCountFrequency (%)
2
 
10.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Other values (9) 9
45.0%
Arabic
ValueCountFrequency (%)
ا 9
24.3%
ج 3
 
8.1%
ر 3
 
8.1%
د 3
 
8.1%
ي 3
 
8.1%
ل 3
 
8.1%
و 2
 
5.4%
ك 2
 
5.4%
ع 2
 
5.4%
ز 2
 
5.4%
Other values (5) 5
13.5%
Cyrillic
ValueCountFrequency (%)
к 1
20.0%
и 1
20.0%
л 1
20.0%
е 1
20.0%
г 1
20.0%
Greek
ValueCountFrequency (%)
ύ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 218745
99.7%
None 353
 
0.2%
CJK 75
 
< 0.1%
Punctuation 47
 
< 0.1%
Arabic 37
 
< 0.1%
Katakana 24
 
< 0.1%
Hangul 20
 
< 0.1%
Cyrillic 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
28949
 
13.2%
e 20370
 
9.3%
a 13261
 
6.1%
o 13144
 
6.0%
i 11174
 
5.1%
t 10150
 
4.6%
n 10052
 
4.6%
r 8990
 
4.1%
l 6754
 
3.1%
s 6708
 
3.1%
Other values (75) 89193
40.8%
None
ValueCountFrequency (%)
é 81
22.9%
í 52
14.7%
ó 51
14.4%
á 40
11.3%
ñ 34
9.6%
ú 26
 
7.4%
¿ 9
 
2.5%
ë 6
 
1.7%
Ü 5
 
1.4%
ê 5
 
1.4%
Other values (25) 44
12.5%
Punctuation
ValueCountFrequency (%)
35
74.5%
4
 
8.5%
4
 
8.5%
3
 
6.4%
1
 
2.1%
Arabic
ValueCountFrequency (%)
ا 9
24.3%
ج 3
 
8.1%
ر 3
 
8.1%
د 3
 
8.1%
ي 3
 
8.1%
ل 3
 
8.1%
و 2
 
5.4%
ك 2
 
5.4%
ع 2
 
5.4%
ز 2
 
5.4%
Other values (5) 5
13.5%
CJK
ValueCountFrequency (%)
2
 
2.7%
2
 
2.7%
2
 
2.7%
2
 
2.7%
2
 
2.7%
2
 
2.7%
1
 
1.3%
1
 
1.3%
1
 
1.3%
1
 
1.3%
Other values (59) 59
78.7%
Katakana
ValueCountFrequency (%)
2
 
8.3%
2
 
8.3%
2
 
8.3%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
Other values (11) 11
45.8%
Hangul
ValueCountFrequency (%)
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Other values (10) 10
50.0%
Cyrillic
ValueCountFrequency (%)
к 1
20.0%
и 1
20.0%
л 1
20.0%
е 1
20.0%
г 1
20.0%

song_popularity
Real number (ℝ)

Distinct101
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.071905
Minimum0
Maximum100
Zeros173
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2022-11-24T23:19:10.985872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q140
median56
Q369
95-th percentile85
Maximum100
Range100
Interquartile range (IQR)29

Descriptive statistics

Standard deviation21.769673
Coefficient of variation (CV)0.41019204
Kurtosis-0.16022173
Mean53.071905
Median Absolute Deviation (MAD)14
Skewness-0.50484316
Sum699700
Variance473.91867
MonotonicityNot monotonic
2022-11-24T23:19:11.186115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58 292
 
2.2%
60 279
 
2.1%
63 270
 
2.0%
66 270
 
2.0%
70 269
 
2.0%
55 269
 
2.0%
62 267
 
2.0%
52 266
 
2.0%
61 261
 
2.0%
53 260
 
2.0%
Other values (91) 10481
79.5%
ValueCountFrequency (%)
0 173
1.3%
1 75
0.6%
2 76
0.6%
3 47
 
0.4%
4 67
 
0.5%
5 53
 
0.4%
6 50
 
0.4%
7 59
 
0.4%
8 61
 
0.5%
9 40
 
0.3%
ValueCountFrequency (%)
100 7
 
0.1%
99 10
 
0.1%
98 32
0.2%
97 27
0.2%
96 36
0.3%
95 42
0.3%
94 54
0.4%
93 22
 
0.2%
92 43
0.3%
91 66
0.5%

song_duration_ms
Real number (ℝ)

Distinct9117
Distinct (%)69.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean218513.6
Minimum12000
Maximum1799346
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2022-11-24T23:19:11.395251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum12000
5-th percentile141970.7
Q1184876.5
median211794
Q3243160
95-th percentile313545.25
Maximum1799346
Range1787346
Interquartile range (IQR)58283.5

Descriptive statistics

Standard deviation60074.785
Coefficient of variation (CV)0.27492469
Kurtosis52.991666
Mean218513.6
Median Absolute Deviation (MAD)28806
Skewness3.4821151
Sum2.8808833 × 109
Variance3.6089797 × 109
MonotonicityNot monotonic
2022-11-24T23:19:11.593418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165000 19
 
0.1%
180000 16
 
0.1%
195000 14
 
0.1%
152000 14
 
0.1%
213309 13
 
0.1%
179404 12
 
0.1%
212500 12
 
0.1%
189000 12
 
0.1%
210367 11
 
0.1%
121346 11
 
0.1%
Other values (9107) 13050
99.0%
ValueCountFrequency (%)
12000 1
< 0.1%
31373 1
< 0.1%
35920 1
< 0.1%
50014 1
< 0.1%
50508 1
< 0.1%
50573 1
< 0.1%
53066 1
< 0.1%
54539 1
< 0.1%
55213 1
< 0.1%
55720 1
< 0.1%
ValueCountFrequency (%)
1799346 1
< 0.1%
1233666 1
< 0.1%
1047933 1
< 0.1%
833493 1
< 0.1%
829586 1
< 0.1%
805746 1
< 0.1%
760973 1
< 0.1%
747222 1
< 0.1%
745653 1
< 0.1%
736160 1
< 0.1%

acousticness
Real number (ℝ)

Distinct2918
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2586286
Minimum1.02 × 10-6
Maximum0.996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2022-11-24T23:19:11.812614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.02 × 10-6
5-th percentile0.00070945
Q10.0251
median0.132
Q30.422
95-th percentile0.883
Maximum0.996
Range0.99599898
Interquartile range (IQR)0.3969

Descriptive statistics

Standard deviation0.28860154
Coefficient of variation (CV)1.1158918
Kurtosis-0.088061909
Mean0.2586286
Median Absolute Deviation (MAD)0.12577
Skewness1.0745901
Sum3409.7595
Variance0.083290851
MonotonicityNot monotonic
2022-11-24T23:19:12.017882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.13 32
 
0.2%
0.105 29
 
0.2%
0.153 29
 
0.2%
0.0215 28
 
0.2%
0.107 28
 
0.2%
0.102 28
 
0.2%
0.173 28
 
0.2%
0.135 26
 
0.2%
0.141 26
 
0.2%
0.119 26
 
0.2%
Other values (2908) 12904
97.9%
ValueCountFrequency (%)
1.02e-06 1
< 0.1%
1.37e-06 1
< 0.1%
1.4e-06 1
< 0.1%
1.8e-06 1
< 0.1%
1.95e-06 1
< 0.1%
2.18e-06 1
< 0.1%
2.42e-06 1
< 0.1%
3.19e-06 1
< 0.1%
3.26e-06 1
< 0.1%
3.41e-06 1
< 0.1%
ValueCountFrequency (%)
0.996 10
0.1%
0.995 18
0.1%
0.994 12
0.1%
0.993 19
0.1%
0.992 9
0.1%
0.991 10
0.1%
0.99 11
0.1%
0.989 6
 
< 0.1%
0.988 10
0.1%
0.987 5
 
< 0.1%

danceability
Real number (ℝ)

Distinct824
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63378368
Minimum0
Maximum0.987
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2022-11-24T23:19:12.229127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.357
Q10.534
median0.646
Q30.748
95-th percentile0.875
Maximum0.987
Range0.987
Interquartile range (IQR)0.214

Descriptive statistics

Standard deviation0.15583833
Coefficient of variation (CV)0.24588567
Kurtosis-0.047375405
Mean0.63378368
Median Absolute Deviation (MAD)0.106
Skewness-0.39539975
Sum8355.804
Variance0.024285584
MonotonicityNot monotonic
2022-11-24T23:19:12.444066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.687 50
 
0.4%
0.657 50
 
0.4%
0.6 49
 
0.4%
0.659 49
 
0.4%
0.6940000000000001 48
 
0.4%
0.643 47
 
0.4%
0.757 47
 
0.4%
0.611 46
 
0.3%
0.755 45
 
0.3%
0.708 44
 
0.3%
Other values (814) 12709
96.4%
ValueCountFrequency (%)
0.0 2
< 0.1%
0.0617 1
< 0.1%
0.0625 1
< 0.1%
0.066 1
< 0.1%
0.0684 1
< 0.1%
0.0722 1
< 0.1%
0.081 1
< 0.1%
0.0812 1
< 0.1%
0.0833 1
< 0.1%
0.0855 1
< 0.1%
ValueCountFrequency (%)
0.987 1
 
< 0.1%
0.981 1
 
< 0.1%
0.978 1
 
< 0.1%
0.975 2
 
< 0.1%
0.972 1
 
< 0.1%
0.969 1
 
< 0.1%
0.968 1
 
< 0.1%
0.967 6
< 0.1%
0.966 1
 
< 0.1%
0.965 5
< 0.1%

energy
Real number (ℝ)

Distinct1058
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.64565028
Minimum0.00107
Maximum0.997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2022-11-24T23:19:12.660815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.00107
5-th percentile0.234
Q10.512
median0.6745
Q30.816
95-th percentile0.94
Maximum0.997
Range0.99593
Interquartile range (IQR)0.304

Descriptive statistics

Standard deviation0.21409813
Coefficient of variation (CV)0.33160077
Kurtosis-0.12181959
Mean0.64565028
Median Absolute Deviation (MAD)0.1505
Skewness-0.62917805
Sum8512.2532
Variance0.04583801
MonotonicityNot monotonic
2022-11-24T23:19:13.083411image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7170000000000001 43
 
0.3%
0.7040000000000001 43
 
0.3%
0.7859999999999999 39
 
0.3%
0.73 39
 
0.3%
0.902 39
 
0.3%
0.63 37
 
0.3%
0.785 37
 
0.3%
0.675 37
 
0.3%
0.716 36
 
0.3%
0.75 36
 
0.3%
Other values (1048) 12798
97.1%
ValueCountFrequency (%)
0.00107 1
< 0.1%
0.00163 1
< 0.1%
0.00205 1
< 0.1%
0.00266 1
< 0.1%
0.00305 1
< 0.1%
0.00344 1
< 0.1%
0.00362 1
< 0.1%
0.00379 2
< 0.1%
0.00419 1
< 0.1%
0.00465 1
< 0.1%
ValueCountFrequency (%)
0.997 3
 
< 0.1%
0.996 5
 
< 0.1%
0.995 2
 
< 0.1%
0.994 3
 
< 0.1%
0.993 5
 
< 0.1%
0.992 3
 
< 0.1%
0.991 12
0.1%
0.99 10
0.1%
0.989 13
0.1%
0.988 12
0.1%

instrumentalness
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct3410
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.077063284
Minimum0
Maximum0.997
Zeros5043
Zeros (%)38.3%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2022-11-24T23:19:13.293659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.085 × 10-5
Q30.0025325
95-th percentile0.78085
Maximum0.997
Range0.997
Interquartile range (IQR)0.0025325

Descriptive statistics

Standard deviation0.22045765
Coefficient of variation (CV)2.8607351
Kurtosis7.7726789
Mean0.077063284
Median Absolute Deviation (MAD)1.085 × 10-5
Skewness3.0180309
Sum1016.0023
Variance0.048601574
MonotonicityNot monotonic
2022-11-24T23:19:13.502582image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5043
38.3%
3.33e-06 23
 
0.2%
1.16e-06 14
 
0.1%
0.00107 14
 
0.1%
4.91e-06 13
 
0.1%
0.00125 13
 
0.1%
1.03e-05 13
 
0.1%
0.00114 12
 
0.1%
1.07e-05 12
 
0.1%
0.391 11
 
0.1%
Other values (3400) 8016
60.8%
ValueCountFrequency (%)
0.0 5043
38.3%
1e-06 2
 
< 0.1%
1.01e-06 3
 
< 0.1%
1.02e-06 6
 
< 0.1%
1.03e-06 5
 
< 0.1%
1.04e-06 8
 
0.1%
1.05e-06 3
 
< 0.1%
1.06e-06 3
 
< 0.1%
1.07e-06 6
 
< 0.1%
1.08e-06 4
 
< 0.1%
ValueCountFrequency (%)
0.997 1
< 0.1%
0.989 1
< 0.1%
0.982 1
< 0.1%
0.979 1
< 0.1%
0.975 1
< 0.1%
0.974 1
< 0.1%
0.973 2
< 0.1%
0.972 1
< 0.1%
0.971 1
< 0.1%
0.969 2
< 0.1%

key
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.260088
Minimum0
Maximum11
Zeros1520
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2022-11-24T23:19:13.671804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6160569
Coefficient of variation (CV)0.68745178
Kurtosis-1.3126961
Mean5.260088
Median Absolute Deviation (MAD)3
Skewness0.010493256
Sum69349
Variance13.075867
MonotonicityNot monotonic
2022-11-24T23:19:13.814397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 1576
12.0%
0 1520
11.5%
7 1430
10.8%
2 1207
9.2%
9 1178
8.9%
11 1129
8.6%
5 1085
8.2%
6 966
7.3%
8 931
7.1%
4 922
7.0%
Other values (2) 1240
9.4%
ValueCountFrequency (%)
0 1520
11.5%
1 1576
12.0%
2 1207
9.2%
3 347
 
2.6%
4 922
7.0%
5 1085
8.2%
6 966
7.3%
7 1430
10.8%
8 931
7.1%
9 1178
8.9%
ValueCountFrequency (%)
11 1129
8.6%
10 893
6.8%
9 1178
8.9%
8 931
7.1%
7 1430
10.8%
6 966
7.3%
5 1085
8.2%
4 922
7.0%
3 347
 
2.6%
2 1207
9.2%

liveness
Real number (ℝ)

Distinct1351
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17880143
Minimum0.0109
Maximum0.986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2022-11-24T23:19:14.016324image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0109
5-th percentile0.0572
Q10.0927
median0.121
Q30.22
95-th percentile0.463
Maximum0.986
Range0.9751
Interquartile range (IQR)0.1273

Descriptive statistics

Standard deviation0.14366175
Coefficient of variation (CV)0.80347092
Kurtosis5.8235153
Mean0.17880143
Median Absolute Deviation (MAD)0.04105
Skewness2.2244285
Sum2357.3181
Variance0.020638699
MonotonicityNot monotonic
2022-11-24T23:19:14.222348image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.108 146
 
1.1%
0.106 141
 
1.1%
0.104 140
 
1.1%
0.102 135
 
1.0%
0.111 135
 
1.0%
0.11 132
 
1.0%
0.112 132
 
1.0%
0.109 131
 
1.0%
0.107 130
 
1.0%
0.105 125
 
0.9%
Other values (1341) 11837
89.8%
ValueCountFrequency (%)
0.0109 1
 
< 0.1%
0.0148 1
 
< 0.1%
0.0186 1
 
< 0.1%
0.0193 1
 
< 0.1%
0.0196 3
< 0.1%
0.0206 1
 
< 0.1%
0.0219 1
 
< 0.1%
0.0222 7
0.1%
0.0233 2
 
< 0.1%
0.0237 1
 
< 0.1%
ValueCountFrequency (%)
0.986 1
 
< 0.1%
0.981 1
 
< 0.1%
0.979 1
 
< 0.1%
0.978 2
< 0.1%
0.974 1
 
< 0.1%
0.967 1
 
< 0.1%
0.961 1
 
< 0.1%
0.956 1
 
< 0.1%
0.952 3
< 0.1%
0.95 2
< 0.1%

loudness
Real number (ℝ)

Distinct6976
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.4452273
Minimum-38.768
Maximum1.585
Zeros0
Zeros (%)0.0%
Negative13178
Negative (%)> 99.9%
Memory size116.0 KiB
2022-11-24T23:19:14.433524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-38.768
5-th percentile-14.3307
Q1-9.03125
median-6.5505
Q3-4.897
95-th percentile-3.154
Maximum1.585
Range40.353
Interquartile range (IQR)4.13425

Descriptive statistics

Standard deviation3.8446346
Coefficient of variation (CV)-0.51638915
Kurtosis6.7099718
Mean-7.4452273
Median Absolute Deviation (MAD)1.9185
Skewness-1.9561566
Sum-98157.877
Variance14.781215
MonotonicityNot monotonic
2022-11-24T23:19:14.653235image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.9910000000000005 14
 
0.1%
-4.884 13
 
0.1%
-6.044 13
 
0.1%
-6.246 12
 
0.1%
-3.714 12
 
0.1%
-6.439 12
 
0.1%
-5.335 12
 
0.1%
-9.127 12
 
0.1%
-4.589 12
 
0.1%
-4.206 12
 
0.1%
Other values (6966) 13060
99.1%
ValueCountFrequency (%)
-38.768 1
< 0.1%
-36.729 1
< 0.1%
-36.281 1
< 0.1%
-35.449 1
< 0.1%
-35.389 1
< 0.1%
-34.255 1
< 0.1%
-33.859 1
< 0.1%
-33.246 1
< 0.1%
-32.195 1
< 0.1%
-31.921 1
< 0.1%
ValueCountFrequency (%)
1.585 1
< 0.1%
1.342 1
< 0.1%
0.878 1
< 0.1%
0.525 1
< 0.1%
0.198 1
< 0.1%
0.119 1
< 0.1%
-0.257 1
< 0.1%
-0.398 1
< 0.1%
-0.737 1
< 0.1%
-0.7390000000000001 1
< 0.1%

audio_mode
Real number (ℝ)

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62818568
Minimum0
Maximum1
Zeros4902
Zeros (%)37.2%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2022-11-24T23:19:14.840898image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.48330751
Coefficient of variation (CV)0.76937047
Kurtosis-1.7187962
Mean0.62818568
Median Absolute Deviation (MAD)0
Skewness-0.53053232
Sum8282
Variance0.23358615
MonotonicityNot monotonic
2022-11-24T23:19:14.979966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1 8282
62.8%
0 4902
37.2%
ValueCountFrequency (%)
0 4902
37.2%
1 8282
62.8%
ValueCountFrequency (%)
1 8282
62.8%
0 4902
37.2%

speechiness
Real number (ℝ)

Distinct1192
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10120507
Minimum0
Maximum0.94
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2022-11-24T23:19:15.167009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0289
Q10.0378
median0.0553
Q30.117
95-th percentile0.33285
Maximum0.94
Range0.94
Interquartile range (IQR)0.0792

Descriptive statistics

Standard deviation0.10401039
Coefficient of variation (CV)1.0277191
Kurtosis7.0209722
Mean0.10120507
Median Absolute Deviation (MAD)0.0224
Skewness2.3411287
Sum1334.2877
Variance0.010818161
MonotonicityNot monotonic
2022-11-24T23:19:15.374138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0337 46
 
0.3%
0.032 46
 
0.3%
0.031 44
 
0.3%
0.109 44
 
0.3%
0.0318 42
 
0.3%
0.0362 41
 
0.3%
0.0439 40
 
0.3%
0.0394 40
 
0.3%
0.038 39
 
0.3%
0.0357 39
 
0.3%
Other values (1182) 12763
96.8%
ValueCountFrequency (%)
0.0 2
< 0.1%
0.0228 2
< 0.1%
0.0229 1
 
< 0.1%
0.0231 3
< 0.1%
0.0234 2
< 0.1%
0.0235 1
 
< 0.1%
0.0236 3
< 0.1%
0.0238 2
< 0.1%
0.0239 3
< 0.1%
0.024 1
 
< 0.1%
ValueCountFrequency (%)
0.94 1
< 0.1%
0.936 1
< 0.1%
0.915 1
< 0.1%
0.906 1
< 0.1%
0.894 1
< 0.1%
0.8909999999999999 1
< 0.1%
0.8690000000000001 1
< 0.1%
0.831 2
< 0.1%
0.83 1
< 0.1%
0.826 1
< 0.1%

tempo
Real number (ℝ)

Distinct9331
Distinct (%)70.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.00792
Minimum0
Maximum214.686
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2022-11-24T23:19:15.587311image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile79.10505
Q198.8705
median120.013
Q3139.934
95-th percentile174.07265
Maximum214.686
Range214.686
Interquartile range (IQR)41.0635

Descriptive statistics

Standard deviation28.663796
Coefficient of variation (CV)0.23687538
Kurtosis-0.21119189
Mean121.00792
Median Absolute Deviation (MAD)20.035
Skewness0.42991707
Sum1595368.4
Variance821.61319
MonotonicityNot monotonic
2022-11-24T23:19:15.783459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120.013 14
 
0.1%
123.07 14
 
0.1%
128.03799999999998 13
 
0.1%
150.036 12
 
0.1%
125.978 12
 
0.1%
149.989 12
 
0.1%
97.064 11
 
0.1%
136.048 11
 
0.1%
95.948 11
 
0.1%
122.007 11
 
0.1%
Other values (9321) 13063
99.1%
ValueCountFrequency (%)
0.0 2
< 0.1%
46.591 1
< 0.1%
51.607 1
< 0.1%
56.983 1
< 0.1%
56.985 1
< 0.1%
57.0 1
< 0.1%
57.304 1
< 0.1%
57.523 1
< 0.1%
58.017 1
< 0.1%
59.54 1
< 0.1%
ValueCountFrequency (%)
214.686 1
< 0.1%
213.99 1
< 0.1%
213.226 1
< 0.1%
212.058 1
< 0.1%
211.644 1
< 0.1%
211.357 1
< 0.1%
210.75 1
< 0.1%
209.421 1
< 0.1%
208.969 1
< 0.1%
208.706 1
< 0.1%

time_signature
Real number (ℝ)

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9604066
Minimum0
Maximum5
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2022-11-24T23:19:15.938138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q14
median4
Q34
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.29858858
Coefficient of variation (CV)0.075393417
Kurtosis48.847344
Mean3.9604066
Median Absolute Deviation (MAD)0
Skewness-5.2255356
Sum52214
Variance0.089155141
MonotonicityNot monotonic
2022-11-24T23:19:16.079289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
4 12457
94.5%
3 511
 
3.9%
5 160
 
1.2%
1 53
 
0.4%
0 3
 
< 0.1%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 53
 
0.4%
3 511
 
3.9%
4 12457
94.5%
5 160
 
1.2%
ValueCountFrequency (%)
5 160
 
1.2%
4 12457
94.5%
3 511
 
3.9%
1 53
 
0.4%
0 3
 
< 0.1%

audio_valence
Real number (ℝ)

Distinct1181
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52841471
Minimum0
Maximum0.984
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2022-11-24T23:19:16.265509image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.12815
Q10.332
median0.528
Q30.728
95-th percentile0.92485
Maximum0.984
Range0.984
Interquartile range (IQR)0.396

Descriptive statistics

Standard deviation0.24640268
Coefficient of variation (CV)0.46630548
Kurtosis-0.99086456
Mean0.52841471
Median Absolute Deviation (MAD)0.198
Skewness-0.022314421
Sum6966.6195
Variance0.060714279
MonotonicityNot monotonic
2022-11-24T23:19:16.484379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961 52
 
0.4%
0.964 45
 
0.3%
0.505 39
 
0.3%
0.591 39
 
0.3%
0.399 36
 
0.3%
0.962 34
 
0.3%
0.376 34
 
0.3%
0.5329999999999999 33
 
0.3%
0.96 32
 
0.2%
0.329 31
 
0.2%
Other values (1171) 12809
97.2%
ValueCountFrequency (%)
0.0 2
< 0.1%
0.023 1
< 0.1%
0.0277 1
< 0.1%
0.0292 2
< 0.1%
0.0312 1
< 0.1%
0.0316 1
< 0.1%
0.0321 1
< 0.1%
0.0326 2
< 0.1%
0.0329 1
< 0.1%
0.033 1
< 0.1%
ValueCountFrequency (%)
0.984 1
 
< 0.1%
0.982 3
< 0.1%
0.981 2
< 0.1%
0.98 1
 
< 0.1%
0.979 2
< 0.1%
0.978 1
 
< 0.1%
0.977 3
< 0.1%
0.976 4
< 0.1%
0.975 3
< 0.1%
0.974 2
< 0.1%

Interactions

2022-11-24T23:19:07.028415image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T23:18:31.954685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T23:18:34.517497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T23:18:37.098009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T23:18:39.673925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T23:18:42.109601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T23:18:44.537169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T23:18:47.141317image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T23:18:49.599904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T23:18:51.975306image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T23:18:54.406351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T23:18:57.225104image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T23:18:59.643564image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-11-24T23:19:09.298164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T23:18:34.352283image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T23:18:36.941476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T23:18:39.503706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T23:18:41.944932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T23:18:44.373941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T23:18:46.985247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T23:18:49.440354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T23:18:51.820125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T23:18:54.251740image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T23:18:57.059578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T23:18:59.488567image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T23:19:01.882952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T23:19:04.257534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-24T23:19:06.870810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-11-24T23:19:16.681501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-11-24T23:19:16.999191image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-24T23:19:17.319976image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-24T23:19:17.634784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-24T23:19:18.169413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-24T23:19:09.549022image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-24T23:19:09.958533image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

song_idsong_namesong_popularitysong_duration_msacousticnessdanceabilityenergyinstrumentalnesskeylivenessloudnessaudio_modespeechinesstempotime_signatureaudio_valence
02231Maneater - Radio Edit582709460.01530.7280.5180.000042110.103-11.11400.03888.70840.833
118680Better Off Alone621928350.01550.9030.4980.00000880.105-6.37910.108121.97440.439
216908Song That I Heard592376660.7730.2670.3560.0017680.167-12.10310.031792.27240.314
310155Zumba602639730.002050.7290.8940.02150.128-3.49410.0397124.99240.832
418150Bumper To Bumper312259330.1480.7710.7350.050.288-9.16200.116119.98340.385
516694Eighties382311330.0002170.4040.9460.0016820.411-11.56200.088151.71640.437
6331I'm A Man141761600.0380.550.9290.090.135-5.76510.0815129.01140.643
718702Body861632160.04760.7520.7640.00009410.0543-4.39910.038121.95840.582
816364God's Gonna Cut You Down661585730.8770.6110.4790.00000650.106-8.07400.11782.36640.79
917995Peaches N Cream482842130.02470.830.7220.00000100.0741-6.09910.0595109.98740.29
song_idsong_namesong_popularitysong_duration_msacousticnessdanceabilityenergyinstrumentalnesskeylivenessloudnessaudio_modespeechinesstempotime_signatureaudio_valence
131749977Confidently Lost692098850.2720.6660.390.00000950.111-8.57510.0838113.86640.325
1317514440El Gato Volador541138670.05290.9580.8070.00065460.924-8.79310.241109.23940.87
131768554Black or White - Single Version712028530.08240.7410.8940.052740.089-3.82610.0495114.86940.96
131777280WEAKEND471678000.2620.6280.5050.0035500.132-8.09110.164137.0440.0679
131787462Leave Me Alone691956370.1070.7920.7430.070.183-2.80610.0851150.02440.742
131798730WORKIN ME861696200.1820.790.6290.00000780.338-4.05510.142170.02340.267
1318017365Nobody Wants to Be Lonely - Ricky Martin with Christina Aguilera532527060.005790.6350.8540.0083100.0623-5.0200.0612100.85140.59
131813236Sinking Ship532474930.950.370.1740.002940.11-19.31600.037792.7540.181
131823161The Light761796530.01780.830.5770.00008140.0436-5.15600.25692.97340.63
131838240Do Wah Diddy Diddy641434000.2620.6610.5570.040.0557-8.24410.049125.44540.957